Dariusz L
e4c3b0fb6f
Add core/type_utils.py with as_dict/as_list helpers, and core/progress.py with a unified report_progress() callback. Replace inline duplicated implementations of _json_safe_metadata(), _as_list(), normalize_filename(), and per-source _report_progress() with calls to the shared modules across downloader, matcher, resolver, and all source modules (CivitAI, CivArchive, HuggingFace, LoRA Manager). Extend filename normalization to include dots as separators.
1076 lines
37 KiB
Python
1076 lines
37 KiB
Python
"""
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Core Resolver Module
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Integrates all components to provide high-level API for model linking.
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"""
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import os
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import re
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import json
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from typing import Dict, Any, List, Optional, Tuple, Callable
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from urllib.parse import unquote
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from .log_system.log_funcs import (
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log_debug,
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log_info,
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log_warn,
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log_error,
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log_exception,
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)
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from .scanner import get_model_files
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from .workflow_analyzer import (
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NESTED_MODEL_KEYS,
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NODE_TYPE_TO_CATEGORY_HINTS,
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analyze_workflow_models,
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get_model_widget_category_hint,
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identify_missing_models,
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should_scan_as_model_reference,
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)
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from .matcher import find_matches
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from .type_utils import as_dict, as_list
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from .workflow_updater import update_workflow_nodes
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from .sources.civitai import resolve_urn
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from .sources.huggingface import parse_huggingface_url as parse_hf_url
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# Regex patterns for URL extraction (matches HuggingFace and CivitAI URLs)
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URL_PATTERN = re.compile(r'(https?://(?:huggingface\.co|civitai\.com)[^\s"\'<>\)\\]+)')
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# Model file extensions to look for
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MODEL_EXTENSIONS = (".safetensors", ".ckpt", ".pt", ".pth", ".bin", ".onnx", ".gguf")
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from .path_utils import (
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get_path_identity,
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get_path_abs,
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get_path_key,
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is_path_within,
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prefer_local_base_directory,
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dedupe_local_base_directories,
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)
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def strip_known_model_extension(filename: str) -> str:
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"""Strip only known model extensions, preserving names like v4.0."""
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if not isinstance(filename, str):
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return ""
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lowered = filename.lower()
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for ext in MODEL_EXTENSIONS:
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if lowered.endswith(ext):
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return filename[: -len(ext)]
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return filename
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def get_workflow_url_info_for_filename(
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workflow_urls: Dict[str, Dict[str, Any]], filename: str
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) -> Optional[Dict[str, Any]]:
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if filename in workflow_urls:
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return workflow_urls[filename]
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filename_stem = strip_known_model_extension(os.path.basename(filename)).lower()
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if not filename_stem:
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return None
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for workflow_filename, url_info in workflow_urls.items():
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workflow_stem = strip_known_model_extension(
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os.path.basename(workflow_filename)
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).lower()
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if workflow_stem == filename_stem:
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return url_info
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return None
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def workflow_has_nodes(workflow_json: Dict[str, Any]) -> bool:
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"""Return True when the active top-level workflow contains nodes."""
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if not isinstance(workflow_json, dict):
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return False
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nodes = workflow_json.get("nodes")
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return isinstance(nodes, list) and len(nodes) > 0
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def iter_active_workflow_nodes(workflow_json: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""Return top-level nodes plus nodes from subgraphs referenced by top-level nodes."""
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if not isinstance(workflow_json, dict):
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return []
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top_level_nodes = workflow_json.get("nodes")
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if not isinstance(top_level_nodes, list):
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return []
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active_nodes = [
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node for node in top_level_nodes if isinstance(node, dict)
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]
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definitions = workflow_json.get("definitions")
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if not isinstance(definitions, dict):
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return active_nodes
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subgraph_list = definitions.get("subgraphs")
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if not isinstance(subgraph_list, list):
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return active_nodes
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subgraphs = {
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str(subgraph.get("id")): subgraph
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for subgraph in subgraph_list
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if isinstance(subgraph, dict) and subgraph.get("id") is not None
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}
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pending_subgraphs = [
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str(node.get("type"))
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for node in active_nodes
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if str(node.get("type")) in subgraphs
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]
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seen_subgraphs = set()
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while pending_subgraphs:
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subgraph_id = pending_subgraphs.pop(0)
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if subgraph_id in seen_subgraphs:
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continue
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seen_subgraphs.add(subgraph_id)
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subgraph = subgraphs.get(subgraph_id)
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subgraph_nodes = subgraph.get("nodes") if isinstance(subgraph, dict) else None
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if not isinstance(subgraph_nodes, list):
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continue
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for node in subgraph_nodes:
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if not isinstance(node, dict):
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continue
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active_nodes.append(node)
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nested_subgraph_id = str(node.get("type"))
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if (
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nested_subgraph_id in subgraphs
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and nested_subgraph_id not in seen_subgraphs
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):
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pending_subgraphs.append(nested_subgraph_id)
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return active_nodes
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def node_has_potential_model_reference(node: Dict[str, Any]) -> bool:
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"""Detect model-looking widget values without resolving paths or scanning disks."""
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if not isinstance(node, dict):
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return False
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widgets_values = node.get("widgets_values")
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if not isinstance(widgets_values, list) or not widgets_values:
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return False
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node_type = node.get("type", "")
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if (
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node_type in {
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"LoraLoaderV2",
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"Lora Loader (LoraManager)",
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"Lora Stacker (LoraManager)",
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}
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and len(widgets_values) >= 3
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and isinstance(widgets_values[2], list)
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):
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for lora_item in widgets_values[2]:
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if isinstance(lora_item, dict) and str(lora_item.get("name") or "").strip():
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return True
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for idx, value in enumerate(widgets_values):
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model_widget_category_hint = get_model_widget_category_hint(node, idx)
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if should_scan_as_model_reference(
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value, declared_model_widget=bool(model_widget_category_hint)
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):
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return True
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if not isinstance(value, dict):
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continue
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for nested_key in NESTED_MODEL_KEYS:
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nested_value = value.get(nested_key)
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if (
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isinstance(nested_value, str)
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and should_scan_as_model_reference(
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nested_value, declared_model_widget=True
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)
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):
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return True
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return False
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def workflow_has_potential_model_references(workflow_json: Dict[str, Any]) -> bool:
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"""Return True when active workflow nodes contain any model-looking values."""
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return any(
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node_has_potential_model_reference(node)
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for node in iter_active_workflow_nodes(workflow_json)
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)
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def normalize_workflow_download_url(url: str) -> str:
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"""Convert workflow file-page URLs into direct download URLs when possible."""
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if not isinstance(url, str) or not url:
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return url
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# HuggingFace /blob/ pages return HTML. /resolve/ returns the actual file.
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return re.sub(
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r"(https?://huggingface\.co/[^/]+/[^/]+)/blob/([^/]+)/(.+)",
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r"\1/resolve/\2/\3",
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url,
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)
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def workflow_url_points_to_file(url: str, filename: str) -> bool:
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"""Return true when a URL appears to reference the specific model file."""
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if not url or not filename:
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return False
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try:
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decoded_url = unquote(url)
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except Exception:
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decoded_url = url
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return filename in decoded_url or unquote(filename) in decoded_url
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def search_local_matches(
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target_for_matching: str,
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category: Optional[str] = None,
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similarity_threshold: float = 0.0,
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max_matches_per_model: int = 10,
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force_rescan: bool = False,
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) -> List[Dict[str, Any]]:
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"""
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Search local model files using the same matcher as workflow analysis.
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Args:
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target_for_matching: Filename/path to match against local files
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category: Optional category hint to prioritize/filter candidates
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similarity_threshold: Minimum similarity score (0.0 to 1.0)
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max_matches_per_model: Maximum number of matches to return
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Returns:
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Deduplicated list of local matches sorted by similarity
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"""
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available_models = get_model_files(force_rescan=force_rescan)
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candidates = available_models
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if category and category != "unknown":
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candidates = [m for m in available_models if m.get("category") == category]
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candidates.extend(
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[m for m in available_models if m.get("category") != category]
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)
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matches = find_matches(
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target_for_matching,
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candidates,
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threshold=similarity_threshold,
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max_results=max_matches_per_model,
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)
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seen_absolute_paths = {}
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deduplicated_matches = []
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for match in matches:
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model_dict = match["model"]
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absolute_path = model_dict.get("path", "")
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path_identity = get_path_identity(absolute_path) if absolute_path else ""
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dedupe_key = path_identity or os.path.normcase(
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model_dict.get("relative_path", "") or match.get("filename", "")
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)
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if dedupe_key not in seen_absolute_paths:
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seen_absolute_paths[dedupe_key] = match
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deduplicated_matches.append(match)
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else:
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existing_match = seen_absolute_paths[dedupe_key]
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if match["confidence"] > existing_match["confidence"]:
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idx = deduplicated_matches.index(existing_match)
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deduplicated_matches[idx] = match
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seen_absolute_paths[dedupe_key] = match
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return deduplicated_matches
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SHA256_PATTERN = re.compile(r"^[a-fA-F0-9]{64}$")
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def normalize_sha256(value: Any) -> str:
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"""Return a normalized SHA256 hex string or an empty string."""
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if value is None:
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return ""
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text = str(value).strip()
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for prefix in ("sha256:", "sha256="):
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if text.lower().startswith(prefix):
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text = text[len(prefix):].strip()
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return text.lower() if SHA256_PATTERN.match(text) else ""
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def _metadata_sidecar_candidates(model_path: str) -> List[str]:
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if not model_path:
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return []
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directory = os.path.dirname(model_path)
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filename = os.path.basename(model_path)
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stem, _ = os.path.splitext(filename)
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candidates = [
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os.path.join(directory, f"{stem}.metadata.json"),
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f"{model_path}.metadata.json",
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os.path.join(directory, f"{filename}.metadata.json"),
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]
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seen = set()
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unique = []
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for candidate in candidates:
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key = os.path.normcase(os.path.abspath(candidate))
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if key in seen:
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continue
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seen.add(key)
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unique.append(candidate)
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return unique
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def _as_dict(value: Any) -> Dict[str, Any]:
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return as_dict(value)
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def _as_list(value: Any) -> List[Any]:
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return as_list(value)
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def _collect_hashes_from_container(value: Any) -> List[str]:
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container = _as_dict(value)
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hashes = _as_dict(container.get("hashes"))
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return [
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normalize_sha256(candidate)
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for candidate in (
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container.get("sha256"),
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container.get("hash"),
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container.get("SHA256"),
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hashes.get("SHA256"),
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hashes.get("sha256"),
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hashes.get("hash"),
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)
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if normalize_sha256(candidate)
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]
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def _metadata_file_matches_model(file_info: Dict[str, Any], model: Dict[str, Any]) -> bool:
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model_filename = str(model.get("filename") or os.path.basename(model.get("path", ""))).lower()
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model_relative = str(model.get("relative_path") or "").replace("\\", "/").lower()
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model_stem = strip_known_model_extension(os.path.basename(model_filename)).lower()
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candidates = [
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file_info.get("name"),
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file_info.get("filename"),
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file_info.get("path"),
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file_info.get("file_path"),
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]
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for candidate in candidates:
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text = str(candidate or "").replace("\\", "/").lower()
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basename = os.path.basename(text)
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stem = strip_known_model_extension(basename).lower()
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if text and model_relative and text == model_relative:
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return True
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if basename and basename == model_filename:
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return True
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if stem and model_stem and stem == model_stem:
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return True
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return False
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def _extract_model_sha256_from_metadata(
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metadata: Dict[str, Any], model: Dict[str, Any]
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) -> List[str]:
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if not isinstance(metadata, dict):
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return []
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hash_status = str(metadata.get("hash_status") or "").strip().lower()
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if hash_status and hash_status != "completed":
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return []
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values: List[str] = []
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values.extend(_collect_hashes_from_container(metadata))
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values.extend(_collect_hashes_from_container(metadata.get("path_metadata")))
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values.extend(_collect_hashes_from_container(metadata.get("file_info")))
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values.extend(_collect_hashes_from_container(metadata.get("file")))
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nested_file_lists = [
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metadata.get("files"),
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_as_dict(metadata.get("selected_version")).get("files"),
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_as_dict(metadata.get("civitai")).get("files"),
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]
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for file_list in nested_file_lists:
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for file_info in _as_list(file_list):
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if isinstance(file_info, dict) and _metadata_file_matches_model(file_info, model):
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values.extend(_collect_hashes_from_container(file_info))
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return [value for value in _unique_ordered_strings(values) if normalize_sha256(value)]
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def _unique_ordered_strings(values: List[Any]) -> List[str]:
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seen = set()
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unique = []
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for value in values:
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text = str(value or "").strip()
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if not text or text in seen:
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continue
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seen.add(text)
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unique.append(text)
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return unique
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def search_local_matches_by_hash(
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sha256: str,
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category: Optional[str] = None,
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max_matches: int = 20,
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force_rescan: bool = False,
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) -> List[Dict[str, Any]]:
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"""
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Find local models whose sidecar .metadata.json contains the given SHA256.
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This intentionally does not hash model files. It only reads metadata sidecars
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next to models already discovered by the scanner.
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"""
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normalized_hash = normalize_sha256(sha256)
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if not normalized_hash:
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return []
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available_models = get_model_files(force_rescan=force_rescan)
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if category and category != "unknown":
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preferred_models = [
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model for model in available_models if model.get("category") == category
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]
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other_models = [
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model for model in available_models if model.get("category") != category
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]
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available_models = preferred_models + other_models
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matches: List[Dict[str, Any]] = []
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for model in available_models:
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model_path = model.get("path", "")
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if not model_path:
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continue
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metadata_path = next(
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(candidate for candidate in _metadata_sidecar_candidates(model_path) if os.path.exists(candidate)),
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"",
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)
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if not metadata_path:
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continue
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try:
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with open(metadata_path, "r", encoding="utf-8") as handle:
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metadata = json.load(handle)
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except Exception as exc:
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log_debug(f"Could not read metadata sidecar for hash match: {metadata_path} ({exc})")
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continue
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metadata_hashes = _extract_model_sha256_from_metadata(metadata, model)
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if normalized_hash not in metadata_hashes:
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continue
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model_with_metadata = {
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**model,
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"sha256": normalized_hash,
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"metadata_path": metadata_path,
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}
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matches.append(
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{
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"model": model_with_metadata,
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"filename": model.get("filename") or os.path.basename(model_path),
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"similarity": 1.0,
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"confidence": 100.0,
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"match_type": "hash",
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"hash_match": True,
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"hash_source": "metadata",
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"sha256": normalized_hash,
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"metadata_path": metadata_path,
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}
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)
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if max_matches > 0 and len(matches) >= max_matches:
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break
|
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|
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return matches
|
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|
|
|
|
def extract_workflow_urls(workflow_json: Dict[str, Any]) -> Dict[str, Dict[str, Any]]:
|
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"""
|
|
Extract model URLs from workflow JSON.
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Sources:
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1. node.properties.models array - contains {name, url, directory}
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2. Regex extraction from workflow JSON string - finds HuggingFace/CivitAI URLs
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Args:
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workflow_json: Complete workflow JSON dictionary
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Returns:
|
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Dict mapping model filename -> {url, directory, source}
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"""
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url_map = {}
|
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|
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# Convert to string for regex search
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workflow_str = json.dumps(workflow_json)
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|
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# Collect all nodes including from subgraphs
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all_nodes = list(workflow_json.get("nodes", []))
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definitions = workflow_json.get("definitions", {})
|
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subgraphs = definitions.get("subgraphs", [])
|
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for subgraph in subgraphs:
|
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subgraph_nodes = subgraph.get("nodes", [])
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all_nodes.extend(subgraph_nodes)
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|
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# 1. Extract from node.properties.models (authoritative source)
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for node in all_nodes:
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node_type = node.get("type", "")
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properties = node.get("properties", {})
|
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models_list = properties.get("models", [])
|
|
|
|
for model_info in models_list:
|
|
if isinstance(model_info, dict):
|
|
name = model_info.get("name", "")
|
|
url = model_info.get("url", "")
|
|
directory = model_info.get("directory", "")
|
|
|
|
if name and name not in url_map:
|
|
url_map[name] = {
|
|
"url": normalize_workflow_download_url(url),
|
|
"model_url": url,
|
|
"directory": directory,
|
|
"node_type": node_type,
|
|
"source": "node_properties",
|
|
}
|
|
|
|
# 2. Extract URLs via regex from workflow JSON
|
|
urls_found = URL_PATTERN.findall(workflow_str)
|
|
|
|
# Clean URLs (remove trailing characters that may have been captured)
|
|
cleaned_urls = []
|
|
for url in urls_found:
|
|
url = url.split(")")[0].replace("\\n", "").replace("\n", "").strip()
|
|
if url:
|
|
cleaned_urls.append(url)
|
|
|
|
# 3. Extract model filenames via regex
|
|
model_pattern = re.compile(
|
|
r"([\w\-\.%]+\.(?:safetensors|ckpt|pt|pth|bin|onnx|gguf))", re.IGNORECASE
|
|
)
|
|
model_files_raw = model_pattern.findall(workflow_str)
|
|
|
|
# Clean and decode filenames
|
|
model_files = set()
|
|
model_name_map = {} # decoded -> original
|
|
|
|
for model in model_files_raw:
|
|
cleaned = model.strip()
|
|
if cleaned and cleaned[0].isalnum():
|
|
try:
|
|
decoded = unquote(cleaned)
|
|
except Exception:
|
|
decoded = cleaned
|
|
model_files.add(decoded)
|
|
model_name_map[decoded] = cleaned
|
|
|
|
# 4. Match URLs to model filenames
|
|
for model in model_files:
|
|
# Keep authoritative node.properties URLs only when they point to the file.
|
|
# Some workflows store a repo/model-page URL there and a concrete file URL
|
|
# elsewhere in the JSON; in that case the file URL is the usable download.
|
|
if (
|
|
model in url_map
|
|
and url_map[model].get("url")
|
|
and workflow_url_points_to_file(url_map[model].get("url"), model)
|
|
):
|
|
continue
|
|
|
|
original_name = model_name_map.get(model, model)
|
|
|
|
for url in cleaned_urls:
|
|
# Check decoded name in URL
|
|
if model in url:
|
|
if (
|
|
model not in url_map
|
|
or not url_map[model].get("url")
|
|
or not workflow_url_points_to_file(url_map[model].get("url"), model)
|
|
):
|
|
url_map[model] = {
|
|
"url": normalize_workflow_download_url(url),
|
|
"model_url": url,
|
|
"directory": url_map.get(model, {}).get("directory", ""),
|
|
"source": "regex",
|
|
}
|
|
else:
|
|
url_map[model]["url"] = normalize_workflow_download_url(url)
|
|
url_map[model]["model_url"] = url
|
|
url_map[model]["source"] = "regex"
|
|
break
|
|
# Check original (possibly URL-encoded) name in URL
|
|
if original_name in url:
|
|
if (
|
|
model not in url_map
|
|
or not url_map[model].get("url")
|
|
or not workflow_url_points_to_file(
|
|
url_map[model].get("url"), original_name
|
|
)
|
|
):
|
|
url_map[model] = {
|
|
"url": normalize_workflow_download_url(url),
|
|
"model_url": url,
|
|
"directory": url_map.get(model, {}).get("directory", ""),
|
|
"source": "regex",
|
|
}
|
|
else:
|
|
url_map[model]["url"] = normalize_workflow_download_url(url)
|
|
url_map[model]["model_url"] = url
|
|
url_map[model]["source"] = "regex"
|
|
break
|
|
# Check without extension
|
|
model_base = os.path.splitext(model)[0]
|
|
if model_base in url or unquote(model_base) in url:
|
|
if (
|
|
model not in url_map
|
|
or not url_map[model].get("url")
|
|
or not workflow_url_points_to_file(url_map[model].get("url"), model)
|
|
):
|
|
url_map[model] = {
|
|
"url": normalize_workflow_download_url(url),
|
|
"model_url": url,
|
|
"directory": url_map.get(model, {}).get("directory", ""),
|
|
"source": "regex",
|
|
}
|
|
else:
|
|
url_map[model]["url"] = normalize_workflow_download_url(url)
|
|
url_map[model]["model_url"] = url
|
|
url_map[model]["source"] = "regex"
|
|
break
|
|
|
|
return url_map
|
|
|
|
|
|
def parse_huggingface_url(url: str) -> Tuple[Optional[str], Optional[str]]:
|
|
"""
|
|
Extract HuggingFace repo and path from URL.
|
|
|
|
Args:
|
|
url: HuggingFace URL
|
|
|
|
Returns:
|
|
Tuple of (repo_id, file_path) or (None, None) if not valid
|
|
"""
|
|
parsed = parse_hf_url(url)
|
|
if parsed:
|
|
return parsed.get("repo"), parsed.get("filename")
|
|
return None, None
|
|
|
|
|
|
def analyze_and_find_matches(
|
|
workflow_json: Dict[str, Any],
|
|
similarity_threshold: float = 0.0,
|
|
max_matches_per_model: int = 10,
|
|
progress_callback: Optional[Callable[[Dict[str, Any]], None]] = None,
|
|
force_rescan: bool = False,
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
Main entry point: analyze workflow and find matches for missing models.
|
|
|
|
Args:
|
|
workflow_json: Complete workflow JSON dictionary
|
|
similarity_threshold: Minimum similarity score (0.0 to 1.0) for matches
|
|
max_matches_per_model: Maximum number of matches to return per missing model
|
|
force_rescan: If True, bypass the short-lived local model scan cache
|
|
|
|
Returns:
|
|
Dictionary with analysis results:
|
|
{
|
|
'missing_models': [
|
|
{
|
|
'node_id': node ID,
|
|
'node_type': node type,
|
|
'widget_index': widget index,
|
|
'original_path': original path from workflow,
|
|
'category': model category,
|
|
'workflow_url': URL from workflow if found,
|
|
'workflow_directory': directory from workflow if found,
|
|
'matches': [
|
|
{
|
|
'model': model dict from scanner,
|
|
'filename': model filename,
|
|
'similarity': similarity score (0.0-1.0),
|
|
'confidence': confidence percentage (0-100)
|
|
},
|
|
...
|
|
]
|
|
},
|
|
...
|
|
],
|
|
'total_missing': count of missing models,
|
|
'total_models_analyzed': count of all models in workflow
|
|
}
|
|
"""
|
|
if progress_callback:
|
|
progress_callback(
|
|
{
|
|
"stage": "extracting",
|
|
"message": "Extracting workflow model references...",
|
|
"current": 0,
|
|
"total": 0,
|
|
}
|
|
)
|
|
|
|
if (
|
|
not workflow_has_nodes(workflow_json)
|
|
or not workflow_has_potential_model_references(workflow_json)
|
|
):
|
|
if progress_callback:
|
|
progress_callback(
|
|
{
|
|
"stage": "completed",
|
|
"message": "Analysis complete",
|
|
"current": 0,
|
|
"total": 0,
|
|
}
|
|
)
|
|
return {
|
|
"missing_models": [],
|
|
"resolved_models": [],
|
|
"total_resolved": 0,
|
|
"total_missing": 0,
|
|
"total_models_analyzed": 0,
|
|
}
|
|
|
|
# Extract URLs from workflow (node.properties.models + regex)
|
|
workflow_urls = extract_workflow_urls(workflow_json)
|
|
log_debug(f"Extracted {len(workflow_urls)} URLs from workflow")
|
|
|
|
if progress_callback:
|
|
progress_callback(
|
|
{
|
|
"stage": "scanning",
|
|
"message": "Scanning local model index...",
|
|
"current": 0,
|
|
"total": 0,
|
|
}
|
|
)
|
|
|
|
# Analyze workflow to find all model references
|
|
# Get available models
|
|
available_models = get_model_files(force_rescan=force_rescan)
|
|
available_models_by_category = {}
|
|
for model in available_models:
|
|
model_category = model.get("category", "")
|
|
if model_category not in available_models_by_category:
|
|
available_models_by_category[model_category] = []
|
|
available_models_by_category[model_category].append(model)
|
|
|
|
ordered_candidates_cache: Dict[str, List[Dict[str, Any]]] = {}
|
|
|
|
if progress_callback:
|
|
progress_callback(
|
|
{
|
|
"stage": "analyzing",
|
|
"message": "Analyzing workflow nodes...",
|
|
"current": 0,
|
|
"total": 0,
|
|
}
|
|
)
|
|
|
|
# Analyze workflow using the same already-scanned model list
|
|
all_model_refs = analyze_workflow_models(
|
|
workflow_json, available_models=available_models
|
|
)
|
|
|
|
if progress_callback:
|
|
progress_callback(
|
|
{
|
|
"stage": "identifying",
|
|
"message": "Identifying missing models...",
|
|
"current": 0,
|
|
"total": len(all_model_refs),
|
|
}
|
|
)
|
|
|
|
# Identify missing models
|
|
missing_models = identify_missing_models(all_model_refs, available_models)
|
|
resolved_model_refs = [
|
|
model_ref for model_ref in all_model_refs if model_ref.get("exists", False)
|
|
]
|
|
|
|
# Enrich missing models with workflow URLs
|
|
for missing in missing_models:
|
|
original_path = missing.get("original_path", "")
|
|
filename = os.path.basename(original_path)
|
|
|
|
url_info = get_workflow_url_info_for_filename(workflow_urls, filename)
|
|
if url_info:
|
|
missing["workflow_url"] = url_info.get("url", "")
|
|
missing["workflow_model_url"] = url_info.get("model_url", "")
|
|
missing["workflow_directory"] = url_info.get("directory", "")
|
|
missing["url_source"] = url_info.get("source", "")
|
|
|
|
# Handle URNs: mark for async resolution by frontend
|
|
# No sync CivitAI calls here - frontend will fetch asynchronously
|
|
for missing in missing_models:
|
|
if missing.get("is_urn"):
|
|
missing["needs_urn_resolve"] = True
|
|
urn = missing.get("urn")
|
|
if urn:
|
|
missing["urn_model_id"] = urn.get("model_id")
|
|
missing["urn_version_id"] = urn.get("version_id")
|
|
missing["urn_type"] = urn.get("type", "")
|
|
|
|
total_matching_models = len(missing_models) + len(resolved_model_refs)
|
|
if progress_callback:
|
|
progress_callback(
|
|
{
|
|
"stage": "matching",
|
|
"message": "Matching local models...",
|
|
"current": 0,
|
|
"total": total_matching_models,
|
|
}
|
|
)
|
|
|
|
local_match_cache: Dict[Tuple[str, str], List[Dict[str, Any]]] = {}
|
|
|
|
def get_match_target(model_ref: Dict[str, Any]) -> Optional[str]:
|
|
target_for_matching = (
|
|
model_ref.get("original_path")
|
|
or model_ref.get("expected_filename")
|
|
or model_ref.get("name")
|
|
or model_ref.get("filename")
|
|
or model_ref.get("full_path")
|
|
or ""
|
|
)
|
|
|
|
# For URNs, prefer expected_filename for matching
|
|
if model_ref.get("is_urn") and model_ref.get("expected_filename"):
|
|
return model_ref["expected_filename"]
|
|
|
|
if isinstance(target_for_matching, str) and target_for_matching.startswith(
|
|
"urn:air:"
|
|
):
|
|
# Don't fuzzy-match the full URN string against every local model.
|
|
# For worker-asset style URNs, try using the filename-like suffix after "@";
|
|
# otherwise skip local matching until the frontend resolves the URN async.
|
|
urn_suffix = (
|
|
target_for_matching.split("@", 1)[1]
|
|
if "@" in target_for_matching
|
|
else ""
|
|
)
|
|
urn_suffix_ext = os.path.splitext(urn_suffix)[1].lower()
|
|
if urn_suffix and urn_suffix_ext in MODEL_EXTENSIONS:
|
|
return urn_suffix
|
|
return None
|
|
|
|
return target_for_matching
|
|
|
|
def get_match_category(model_ref: Dict[str, Any]) -> str:
|
|
category = model_ref.get("category")
|
|
if not category or category == "unknown":
|
|
node_type = model_ref.get("node_type", "")
|
|
category = NODE_TYPE_TO_CATEGORY_HINTS.get(node_type, "unknown")
|
|
return category or "unknown"
|
|
|
|
def get_candidates_for_category(category: str) -> List[Dict[str, Any]]:
|
|
if not category or category == "unknown":
|
|
return available_models
|
|
|
|
candidates = ordered_candidates_cache.get(category)
|
|
if candidates is None:
|
|
preferred = available_models_by_category.get(category, [])
|
|
others = [
|
|
m for m in available_models if m.get("category") != category
|
|
]
|
|
candidates = preferred + others
|
|
ordered_candidates_cache[category] = candidates
|
|
|
|
return candidates
|
|
|
|
def deduplicate_matches(matches: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
|
|
seen_absolute_paths = {}
|
|
deduplicated_matches = []
|
|
for match in matches:
|
|
model_dict = match["model"]
|
|
absolute_path = model_dict.get("path", "")
|
|
path_identity = get_path_identity(absolute_path) if absolute_path else ""
|
|
dedupe_key = path_identity or os.path.normcase(
|
|
model_dict.get("relative_path", "") or match.get("filename", "")
|
|
)
|
|
|
|
if dedupe_key not in seen_absolute_paths:
|
|
seen_absolute_paths[dedupe_key] = match
|
|
deduplicated_matches.append(match)
|
|
else:
|
|
existing_match = seen_absolute_paths[dedupe_key]
|
|
if match["confidence"] > existing_match["confidence"]:
|
|
idx = deduplicated_matches.index(existing_match)
|
|
deduplicated_matches[idx] = match
|
|
seen_absolute_paths[dedupe_key] = match
|
|
|
|
return deduplicated_matches
|
|
|
|
def find_local_matches_for_ref(model_ref: Dict[str, Any]) -> List[Dict[str, Any]]:
|
|
target_for_matching = get_match_target(model_ref)
|
|
if not target_for_matching:
|
|
return []
|
|
|
|
category = get_match_category(model_ref)
|
|
cache_key = (target_for_matching, category)
|
|
if cache_key in local_match_cache:
|
|
return local_match_cache[cache_key]
|
|
|
|
matches = find_matches(
|
|
target_for_matching,
|
|
get_candidates_for_category(category),
|
|
threshold=similarity_threshold,
|
|
max_results=max_matches_per_model,
|
|
)
|
|
deduplicated_matches = deduplicate_matches(matches)
|
|
local_match_cache[cache_key] = deduplicated_matches
|
|
return deduplicated_matches
|
|
|
|
# Find matches for each missing model
|
|
missing_with_matches = []
|
|
total_missing = len(missing_models)
|
|
for index, missing in enumerate(missing_models, start=1):
|
|
if progress_callback:
|
|
progress_callback(
|
|
{
|
|
"stage": "matching",
|
|
"message": f"Analyzing model {index} of {total_missing}",
|
|
"current": index,
|
|
"total": total_matching_models,
|
|
"model_name": missing.get("name")
|
|
or missing.get("original_path", ""),
|
|
}
|
|
)
|
|
|
|
# Skip LoraManager lorAs that already exist locally (exists=True means no linking needed)
|
|
is_lora_v2 = missing.get("is_lora_v2")
|
|
exists = missing.get("exists")
|
|
name = missing.get("name") or missing.get("original_path", "")
|
|
log_debug(f"Checking {name}: is_lora_v2={is_lora_v2}, exists={exists}")
|
|
|
|
if is_lora_v2 and exists:
|
|
log_info(f"Skipping LoraManager lora {name} - already exists locally")
|
|
continue
|
|
|
|
missing_with_matches.append({
|
|
**missing,
|
|
"matches": find_local_matches_for_ref(missing),
|
|
})
|
|
|
|
resolved_with_matches = []
|
|
total_resolved = len(resolved_model_refs)
|
|
for index, resolved in enumerate(resolved_model_refs, start=1):
|
|
if progress_callback:
|
|
progress_callback(
|
|
{
|
|
"stage": "matching",
|
|
"message": f"Analyzing resolved model {index} of {total_resolved}",
|
|
"current": total_missing + index,
|
|
"total": total_matching_models,
|
|
"model_name": resolved.get("name")
|
|
or resolved.get("original_path", ""),
|
|
}
|
|
)
|
|
|
|
resolved_with_matches.append({
|
|
**resolved,
|
|
"matches": find_local_matches_for_ref(resolved),
|
|
})
|
|
|
|
result = {
|
|
"missing_models": missing_with_matches,
|
|
"resolved_models": resolved_with_matches,
|
|
"total_resolved": len(resolved_with_matches),
|
|
"total_missing": len(missing_with_matches),
|
|
"total_models_analyzed": len(all_model_refs),
|
|
}
|
|
|
|
if progress_callback:
|
|
progress_callback(
|
|
{
|
|
"stage": "completed",
|
|
"message": "Analysis complete",
|
|
"current": total_matching_models,
|
|
"total": total_matching_models,
|
|
}
|
|
)
|
|
|
|
return result
|
|
|
|
|
|
def apply_resolution(
|
|
workflow_json: Dict[str, Any], resolutions: List[Dict[str, Any]]
|
|
) -> Dict[str, Any]:
|
|
"""
|
|
Apply model resolutions to workflow.
|
|
|
|
Args:
|
|
workflow_json: Workflow JSON dictionary (will be modified)
|
|
resolutions: List of resolution dictionaries:
|
|
{
|
|
'node_id': node ID,
|
|
'widget_index': widget index,
|
|
'resolved_path': absolute path to resolved model,
|
|
'category': model category (optional),
|
|
'resolved_model': model dict from scanner (optional),
|
|
'nested_key': nested key for dict-type widgets (optional)
|
|
}
|
|
|
|
Returns:
|
|
Updated workflow JSON dictionary
|
|
"""
|
|
# Prepare mappings for workflow_updater
|
|
mappings = []
|
|
for resolution in resolutions:
|
|
mapping = {
|
|
"node_id": resolution.get("node_id"),
|
|
"widget_index": resolution.get("widget_index"),
|
|
"resolved_path": resolution.get("resolved_path"),
|
|
"category": resolution.get("category"),
|
|
"resolved_model": resolution.get("resolved_model"),
|
|
"subgraph_id": resolution.get(
|
|
"subgraph_id"
|
|
), # Include subgraph_id for subgraph nodes
|
|
"is_top_level": resolution.get(
|
|
"is_top_level"
|
|
), # True for top-level nodes, False for nodes in subgraph definitions
|
|
"is_lora_v2": resolution.get("is_lora_v2"), # Flag for LoraManager nodes
|
|
"original_lora_name": resolution.get(
|
|
"original_lora_name"
|
|
), # Original lora name for LoraManager replacement
|
|
"nested_key": resolution.get(
|
|
"nested_key"
|
|
), # For dict-type widgets (e.g. Power Lora Loader)
|
|
}
|
|
|
|
# If resolved_model provided, extract path if needed
|
|
if "resolved_model" in resolution and resolution["resolved_model"]:
|
|
resolved_model = resolution["resolved_model"]
|
|
if "path" in resolved_model and not mapping.get("resolved_path"):
|
|
mapping["resolved_path"] = resolved_model["path"]
|
|
if "base_directory" in resolved_model:
|
|
mapping["base_directory"] = resolved_model["base_directory"]
|
|
|
|
mappings.append(mapping)
|
|
|
|
# Update workflow
|
|
updated_workflow = update_workflow_nodes(workflow_json, mappings)
|
|
|
|
return updated_workflow
|
|
|
|
|
|
def get_resolution_summary(workflow_json: Dict[str, Any]) -> Dict[str, Any]:
|
|
"""
|
|
Get summary of missing models and matches without applying resolutions.
|
|
|
|
This is a convenience method that calls analyze_and_find_matches with defaults.
|
|
|
|
Args:
|
|
workflow_json: Complete workflow JSON dictionary
|
|
|
|
Returns:
|
|
Same format as analyze_and_find_matches
|
|
"""
|
|
return analyze_and_find_matches(workflow_json)
|